We demonstrate how geological heterogeneity impacts the effectiveness of surfactant-based enhanced oil recovery (EOR) at larger (inter-well and sector) scales when upscaling small (core) scale heterogeneity and physicochemical processes. We used two experimental datasets of surfactant-based EOR where spontaneous imbibition and viscous displacement, respectively dominate recovery. We built 3D core-scale simulation models to match the data and parameterize surfactant models. The results were deployed in high-resolution models that preserve the complexity and heterogeneity of carbonate formations in the inter-well and sector scale. These larger-scale models were based on two outcrop analogues from France and Morroco, respectively, which capture the reservoir architectures inherent to the productive carbonate reservoir systems in the Middle East. We then assessed and quantified the error in production forecast that arises due to upscaling, upgridding, and simplification of geological heterogeneity. Simulation results showed a broad range of recovery predictions. The variability arises from the choice of surfactant model parameterization (i.e., spontaneous imbibition vs viscous displacement) and the way the heterogeneity in the inter-well and sector models was upscaled and simplified. We found that the parameterization of surfactant models has a significant impact on recovery predictions. Oil recovery at the larger scale was observed to be higher when using the parametrization derived from viscous displacement experiments compared to parameterization from spontaneous imbibition experiments. This observation clearly demonstrated how core-scale processes impact recovery predictions at the larger scales. Also, the variability in recovery prediction due to the choice of surfactant model was as large as the variability arising from upscaling and upgridding. Upscaled and upgridded models overestimated recovery because of the simplified geology. Grid coarsening exacerbated this effect because of the increased numerical dispersion. These results emphasize the need to use correctly configured surfactant models, appropriate grid resolution that minimizes numerical dispersion, and properly upscaled reservoir models to accurately forecast surfactant floods. Our findings present new insights into how the uncertainty in production forecasts during surfactant flooding depends on the way surfactant models are parameterized, how the reservoir geology is upscaled, and how numerical dispersion is impacted by grid coarsening.

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We propose a workflow to optimize the configuration of multiple-interacting-continua (MINC) models and overcome the limitations of the classical dual-porosity (DP) model when simulating chemical-component-transport processes during two-phase flow. Our new approach captures the evolution of the saturation and concentration fronts inside the matrix, which is key to design more effective chemical enhanced-oil-recovery (CEOR) projects in naturally fractured reservoirs. Our workflow is intuitive and derived from the simple concept that fine-scale single-porosity (SP) models capture fracture/matrix interaction accurately; it can hence be easily applied in any reservoir simulator with MINC capabilities. Results from the fine-scale SP model are translated into an equivalent MINC model that yields more accurate results compared with a classical DP model for oil recovery by spontaneous imbibition; for example, in a water-wet (WW) case, the root-mean-square error (RMSE) improves from 0.123 to 0.034. In general, improved simulation results can be obtained when selecting five or fewer shells in the MINC model. However, the actual number of shells is case specific. The largest improvement in accuracy is observed for cases where the matrix permeability is low and fracture/matrix transfer remains in a transient state for a prolonged time. The novelty of our approach is the simplicity of defining shells for a MINC model such that the chemical-component-transport process in naturally fractured reservoirs can be predicted more accurately, especially in cases where the matrix has low permeability. Hence, the improved MINC model is particularly suitable to model chemical-component transport, key to many CEOR processes, in (tight) fractured carbonates.

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When CO₂ is injected into an aquifer, the injected CO₂ is generally colder than the reservoir rock; this results in thermal gradients along the flow path. The temperature variation has an impact on CO₂ solubility and the kinetics of any mineral reactions. Core flood experiments and associated reactive transport simulations were conducted to analyse thermal effects during CO₂ injection in a dolomitic limestone aquifer and to quantify how CO₂ solubility and mineral reactivity are affected. The experiments were conducted by injecting acidified brine into an Edwards Limestone core sample. A back pressure of 400 psi and injection rates of 30 mL/hr and 300 mL/hr were used. A range of temperatures from 21 °C to 70 °C were examined. Changes in the outlet fluid composition and changes in porosity and permeability were analysed. A compositional simulation model was used to further analyse the experiments. The simulations were history-matched to the experimental data by changing the reactive surface area and the kinetic rate parameter. The calibrated model was then used to test the sensitivity to CO₂ injection rate and temperature. The impact of temperature on CO₂-induced mineral reactions was observed from changes in mineral volume, porosity and permeability. The reaction rate constants estimated from the outlet solution concentrations are much lower than existing data for individual minerals. The estimated specific surface areas for carbonate minerals are in reasonable agreement with published values. The numerical investigations showed that at the lower temperatures, despite the reaction rates being slower, the solubility of the minerals was higher, and so as a result of these competing effects, moderately elevated calcium and magnesium concentrations were observed in the effluent. At higher temperatures, the solubilities of the minerals were lower, but now the reactions rates were higher, so similar effluent concentrations could be achieved. However, at higher flow rates, characterized by a lower Damköhler number, the residence times were shorter, and so lower effluent concentrations were observed. Additionally, the solubilities of calcite and dolomite varied to different extents with temperature, and so the calcium to magnesium molar ratio in the effluent brine increased with increasing temperature.

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Understanding pore-scale flow and transport processes is important for understanding flow and transport within rocks on a larger scale. Flow experiments on small-scale micromodels can be used to experimentally investigate pore-scale flow. Current manufacturing methods of micromodels are costly and time consuming. 3D printing is an alternative method for the production of micromodels. We have been able to visualise small-scale, single-phase flow and transport processes within a 3D printed micromodel using a custom-built visualisation cell. Results have been compared with the same experiments run on a micromodel with the same geometry made from polymethyl methacrylate (PMMA, also known as Perspex). Numerical simulations of the experiments indicate that differences in experimental results between the 3D printed micromodel and the Perspex micromodel may be due to variability in print geometry and surface properties between the samples. 3D printing technology looks promising as a micromodel manufacturing method; however, further work is needed to improve the accuracy and quality of 3D printed models in terms of geometry and surface roughness.

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We investigate how efficiently oil can be recovered from a carbonate rock during surfactant based enhanced oil recovery (EOR) at the core-scale, particularly when chemical processes change wettability, and analyse how geological heterogeneities, observed at the next larger scale (centimetre to decimetre) impacts the effectiveness of surfactant-based EOR at the inter-well scale. To quantify how heterogeneity across scales impacts surfactant flooding, we combine laboratory experiments with simulation studies at the core- and inter-well scale. We first analysed a series of surfactant imbibition experiments at different surfactant concentrations (from 0 to 3 wt. %) using reservoir cores from the Wakamuk field, a carbonate reservoir in Indonesia. We then built a 3D simulation model of the laboratory experiment and matched the experimental data to identify the key physical mechanisms (e.g., reduction in interfacial tension (IFT) and wettability alteration) that lead to increased oil recovery. Next, we parametrised the surfactant models using assisted history-matching methods to calibrate the relative permeability and capillary pressure curves as a function of surfactant concentration. These models were then deployed in high-resolution simulations at the inter-well scale. These simulations captured the small-scale geological heterogeneities that are typical for a carbonate reservoir system, e.g., the Shuaiba formation in the Middle East, but are not resolved in field-scale models. Our core-scale simulations demonstrate a change from co- to counter-current flow in the laboratory experiments and indicate that the resulting increase in oil recovery is due to a combination of IFT reduction, wettability alteration from oil- to water-wet, and capillary pressure restoration; these processes need to be captured adequately at the inter-well scale model. The increase in surfactant concentration above the critical micelle concentration (CMC) (i.e., from 1 to 3 wt. %) triggered the capillary pressure restoration and dominated recovery at the early-time. The changes in relative permeability and capillary curves during the surfactant floods were best modelled using a concentration-based interpolation. There is uncertainty when calibrating surfactant models using laboratory experiments. A key question hence is if geological heterogeneity at the inter-well scale masks these uncertainties. Results from our high-resolution simulations show that large-scale heterogeneity impacts recovery predictions, but it is the coarsening of the grid, not the upscaling of permeability, that dominates the error in field-scale recovery predictions during surfactant based EOR. Indeed, the error arising from numerical dispersion during grid coarsening can be as large as the error arising when selecting an inaccurately configured surfactant model due to the lack of quality experimental data. Hence appropriate grid refinement, possibly using adaptive grid refinement, needs to be considered when setting up a surfactant based EOR simulation, along with the appropriate configuration of the surfactant model itself.

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We propose a workflow to optimise the configuration of multiple interacting continua (MINC) models and overcome the limitations of the classical dual-porosity model when simulating chemically enhanced oil recovery processes. Our new approach captures the evolution of the concentration front inside the matrix, which is key to design a more effective chemically enhanced oil recovery projects in naturally fractured reservoirs. Our workflow is intuitive and based on the simple concept that fine-scale single-porosity models capture fracture-matrix interaction accurately and can hence be easily applied in a commercial reservoir simulator. Results from the fine-scale single-porosity system are translated into an equivalent MINC method that yields more accurate results than the classical dual-porosity model or a MINC method where the shells are arbitrarily selected. Our approach does not require the tuning of capillary pressure curves ("pseudoisation"), diffusion coefficients, MINC shells, or the generation of recovery type curves, all of which have been suggested in the past to model more complex recovery processes. A careful examination of the fine-scale single-porosity model ("reference case") shows that a number of nested shells emerge, describing the advance of the concentration and saturation fronts inside the matrix. The number of shells is related to the required degree of refinement, i.e. the number of shells, in the improved MINC model. Using the results from a fine-scale single-porosity simulation to set up the shells in the MINC model is easy and requires only simple volume calculations. It is hence independent of the chosen simulator. Our improved MINC method yields significantly more accurate results compared to a classical dual-porosity model, a MINC method with equally sized shells, or a MINC model with arbitrarily refined shells for a number of recovery scenarios that cover a range of matrix wettabilities and permeabilities. In general, improved results can be obtained when selecting five or fewer shells in the MINC. However, the actual number of shells is case-specific. The largest improvement is observed for cases when the matrix permeability is low. The novelty of our approach is the easy-to-use method to define shells for a MINC model to predict chemically enhanced oil recovery from naturally fractured reservoirs more accurately, especially in cases where the matrix has low permeability. Hence the improved MINC method is particularly suitable to model chemical EOR processes in (tight) fractured carbonates.

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Three-dimensional (3-D) printing provides a fast, cost-effective way to produce and replicate complicated designs with minimal flaws and little material waste. Early use of 3-D printing for engineering applications in the petroleum industry has stimulated further adoption by geoscience researchers and educators. Recent progress in geoscience is signified by capabilities that translate digital rock models into 3-D printed rock proxies. With a variety of material and geometric scaling options, 3-D printing of nearidentical rock proxies provides a method to conduct repeatable laboratory experiments without destroying natural rock samples. Rock proxy experiments can potentially validate numerical simulations and complement existing laboratory measurements on changes of rock properties over geologic time scales. A review of published research from academic, government, and industry contributions indicates a growing community of rock proxy experimentalists. Three-dimensional printing techniques are being applied to fundamental research in the areas of multiphase fluid flow and reactive transport, geomechanics, physical properties, geomorphology, and paleontology. Further opportunities for geoscience research are discussed. Applications in education include teaching models of terrains, fossils, and crystals. The integration of digital data sets with 3-D printed geomorphologies supports communication for both societal and technical objectives. Broad benefits that could be realized from centralized 3-D printing facilities are also discussed.

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Advanced or smart completion wells are different from conventional wells by being equipped with downhole flow control devices such as Interval Control Valves (ICV) and Inflow Control Devices (ICD) to offer improved reservoir management and control and thus maximise hydrocarbon production and recovery. In order to justify their implementation and increase their economic return, a high degree of robustness in modelling, prediction and optimisation of their performance is required. To improve the robustness of forecasting production from advanced or smart wells using reservoir simulation, high-level details in rock and fluid flow properties are needed in the near-wellbore region to accurately capture the flow dynamics. The paper presents an improved approach that enables us to robustly predict the performance of advanced or smart wells in reservoir simulation and highlights the importance of representing the near-wellbore region when optimizing smart well completions. Performances of advanced or smart well completions are very dependent on changes in flow rate, pressure, and saturations, which mainly occur in areas around the wells. The paper demonstrates the use of local grid refinement (LGR) in the near-wellbore region to enhance the accuracy level of simulation predictions. In the study, an objective function based ICV optimization strategy was used to identify the optimum settings for every time step during the simulation run. We also demonstrate how to correlate ICV settings to Passive Inflow Control Device (PICD) or Autonomous Inflow Control Device (AICD) strengths if a requirement arises to impose the use of ICVs. Using a well-established synthetic reservoir model, we demonstrate how the representation of the near-wellbore region impacts reservoir performance predictions and influences the way ICVs and ICDs are optimized. We observe that by applying this approach, the predicted NPV and recovery factor change by 6.6% and 6.1%, respectively. In addition, this study also quantifies the impact of near-wellbore representation on four completion types; Openhole, ICV, PICD and AICD completions. The novelty of this paper is that it presents an approach to improve production forecasts that supports decision making during field development planning to maximize profit and minimize risk.

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When simulating foam floods, uncertainties exist in both the foam and reservoir parameters however the combination of these uncertainties are rarely incorporated in forecasting. Foam flooding is an effective enhanced oil recovery method that controls mobility, reduces gas relative permeability, delays gas breakthrough and helps improve sweep efficiency. Thus it is often used in highly heterogeneous reservoirs where significant subsurface uncertainties exist. Foam uncertainties exist as (a) foam stability is controlled by a number of factors such as critical water and surfactant concentrations, brine salinity, and oil saturation which are unknown in the subsurface spatially and (b) foam flood simulation requires the accurate description of multiple parameters used in the foam flood models which are unknown. This study quantifies and compares the impact of uncertainties associated with foam model parameters with the heterogeneity of a fractured carbonate reservoir, an analogue to the highly prolific Arab D formation. Foam model parameters are not known a-priori but can be tuned to experimental data, which ideally represent a range of foam regimes and reservoir conditions. Geological heterogeneities in fractured carbonate reservoirs are complex and include, matrix wettability, fracture density/orientation and initial saturation distribution. To quantify uncertainties geological uncertainties in fractured carbonate reservoirs, an automated framework was used to history match the production response of a fractured carbonate field by varying geological parameters. This accounts for the geological uncertainties during a waterflood, which are then combined with foam uncertainties from experimental analysis in the optimisation step, by optimising the mean response of the model to foam flooding across a range of geological and foam scenarios. Our workflow used a combination of Particle Swarm Optimisation for history matching and manual optimisation, the final results of which show a wide range of possible impacts of foam flooding from different but equally well matched reservoirs. The novelty of our work is that it demonstrates how parameters that control foam stability and hence effectiveness in mobility control are related to both foam properties and geological uncertainty. Carrying these uncertainties into foam model properties from core to field scale will translate into considerably more robust estimates of uncertainty when predicting field-scale recovery compared to simulations that only consider uncertainty in the reservoir model.@en

Foam can be used to enhance oil recovery by increasing the areal and lateral sweep, reducing gravity override and diverting the flow from to low permeable areas. In this study we matched two experimental data sets of foam injection in carbonate rocks that were conducted at different pressure and temperature to a numerical model. The aim to analyse how the resulting changes in foam properties affect the overall sweep efficiency in the presence of geological uncertainties that are typical for a fractured carbonate reservoir. In order to quantify how reservoir heterogeneity impacts foam injection at high/low temperature and pressure, and hence the field-scale recovery, we deploy the matched foam models in a reservoir simulation model that is a realistic analogue for the Arab D formation in Qatar. We use this simulation model to test the effect of foam properties, injection strategies, and reservoir properties to analyse the displacement processes and compare the overall sweep and recovery. To this end, foam yields significantly higher recovery factors compared to other EOR methods, in particular if the reservoir is heavily fractured, but the uncertainties in foam properties can affect recovery factors to the same degree as geological uncertainties.@en

Reactive transport simulators are used to investigate the impact of geochemical reactions on in situ brine compositions, and to predict the behaviour of produced brines. These calculations are then used to inform scale management decision making, and to calculate risk of damage and cost of treatment. The authors describe the various types of reservoir interactions that occur during reservoir displacement processes, and then use examples of reactive flow simulations to illustrate the impact of these reservoir processes on scale management decision making and cost, using examples with field data from sandstone and carbonate reservoirs. Barium stripping in sandstone reservoirs undergoing seawater flooding is shown to reduce scaling tendency at the producers, while sulphate stripping due to anhydrite precipitation in a chalk field has an even greater impact, delaying the onset of scaling and reducing chemical treatment costs. The field examples are preceded by a discussion of the velocity at which various saturation, component and thermal fronts propagate, and how mixing regimes are established.@en

Conventional simulation of fractured carbonate reservoirs is computationally expensive because multi-scale heterogeneities and fracture-matrix transfer must be taken into account. The computational requirement increases exponentially when multiple simulation runs are required for sensitivity analysis and quantification of the uncertainty range. This can be prohibitive, especially for giant carbonate reservoirs. Yet, sensitivity analysis and uncertainty quantification are particularly important to analyse, determine and rank the impact of geological and engineering parameters on the economics and sustainability of different EOR techniques. We use experimental design to set up multiple screened simulations of a high-resolution model of a Jurassic Carbonate ramp, which is an analogue for the highly prolific reservoirs of the Arab D formation in Qatar. We consider CO2 water-alternating-gas (WAG) injection, which has been shown to be a successful EOR method for carbonate reservoirs. The simulations are used as a basis for generating polynomial response surfaces for prediction of hydrocarbon recovery and net gas utilisation. We compare response surfaces from polynomial regression to response surfaces generated with polynomial chaos expansion (PCE). PCE allows for non-linear mapping of parameter uncertainty to the predicted results. In the current work, the geological parameter uncertainties affecting WAG modelling in fractured carbonates are evaluated. These include end-member fault transmissibility configurations, wettability scenarios, hysteresis models and fracture intensity. Effective fracture permeabilities are computed using discrete fracture networks (DFN) for sparsely distributed regional fractures. The results enable us to adequately explore the parameter space, quantify and rank the interrelated effect uncertain model parameters on CO2 WAG efficiency in fractured carbonate reservoirs. The results highlight the first order impact of fracture intensity, wettability and hysteresis on hydrocarbon recovery and net gas utilisation. Furthermore, reduced order (i.e. proxy) models enable us to calculate quick estimates of the probabilistic uncertainty range and achieve significant computational speed-up compared with the conventional Monte Carlo framework.@en